The Inconvenient Truth About Data Science
Data is never clean, you will spend most of your time cleaning and preparing data, 95% of tasks do not require deep learning, and more inconvenient wisdom.
By Kamil Bartocha (lastminute.com)
Bio: Kamil Bartocha is Head of Data Science at lastminute.com, and an expert in the field of data processing, data systems architecture and artificial intelligence.
Original.
(Editor: What are your inconvenient truths? Please comment)
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Data is never clean.
- You will spend most of your time cleaning and preparing data.
- 95% of tasks do not require deep learning.
- In 90% of cases generalized linear regression will do the trick.
- Big Data is just a tool.
- You should embrace the Bayesian approach.
- No one cares how you did it.
- Academia and business are two different worlds.
- Presentation is key - be a master of Power Point.
- All models are false, but some are useful.
- There is no fully automated Data Science. You need to get your hands dirty.
Bio: Kamil Bartocha is Head of Data Science at lastminute.com, and an expert in the field of data processing, data systems architecture and artificial intelligence.
Original.
(Editor: What are your inconvenient truths? Please comment)
Related:
- Interview: Alessandro Gagliardi, Glassdoor on the Fun and Boring Part of Data Scientist Job
- Automatic Statistician and the Profoundly Desired Automation for Data Science
- A Data Scientist Advice to Business Schools